Injecting utility into anonymized datasets

Daniel Kifer, Johannes Gehrke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

187 Scopus citations

Abstract

Limiting disclosure in data publishing requires a careful balance between privacy and utility. Information about individuals must not be revealed, but a dataset should still be useful for studying the characteristics of a population. Privacy requirements such as k-anonymity and l-diversity are designed to thwart attacks that attempt to identify individuals in the data and to discover their sensitive information. On the other hand, the utility of such data has not been well-studied.In this paper we will discuss the shortcomings of current heuristic approaches to measuring utility and we will introduce a formal approach to measuring utility. Armed with this utility metric, we will show how to inject additional information into k-anonymous and l-diverse tables. This information has an intuitive semantic meaning, it increases the utility beyond what is possible in the original k-anonymity and l-diversity frameworks, and it maintains the privacy guarantees of k-anonymity and l-diversity.

Original languageEnglish (US)
Title of host publicationSIGMOD 2006 - Proceedings of the ACM SIGMOD International Conference on Management of Data
Pages217-228
Number of pages12
DOIs
StatePublished - Dec 1 2006
Event2006 ACM SIGMOD International Conference on Management of Data - Chicago, IL, United States
Duration: Jun 27 2006Jun 29 2006

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

Other2006 ACM SIGMOD International Conference on Management of Data
CountryUnited States
CityChicago, IL
Period6/27/066/29/06

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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    Kifer, D., & Gehrke, J. (2006). Injecting utility into anonymized datasets. In SIGMOD 2006 - Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 217-228). (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/1142473.1142499